code: def layer_has_enough_weights_to_cluster(
      layer, number_of_clusters, cluster_per_channel
  ):
      """Returns whether layer has enough weights to cluster.
  
      Returns True if Conv2D layer has sufficient number of
      weights to implement clustering, given an input number of clusters.
  
      Args:
        layer: input layer to return quantize configs for.
        number_of_clusters: A number of cluster centroids to form clusters.
        cluster_per_channel: An optional boolean value.
      """
      if not isinstance(layer, tf.keras.layers.Conv2D):
          raise ValueError(
              f"Input layer should be Conv2D layer: {layer.name} given."
          )
  
      if not layer.trainable_weights:
          raise ValueError(
              f"Layer {layer.name} has no weights to cluster."
          )
  
      number_of_layer_weights = tf.cast(
          tf.size(getattr(layer, "kernel")), tf.int32
      )
      channel_idx = 1 if layer.data_format == "channels_first" else -1
      number_of_channels = tf.size(layer.trainable_weights[channel_idx])
  
      if cluster_per_channel:
          weights_to_cluster = (
              number_of_layer_weights / number_of_channels
          )
      else:
          weights_to_cluster = number_of_layer_weights
  
      if weights_to_cluster <= number_of_clusters:
          has_enough_weights = False
      else:
          has_enough_weights = True
  
      if not has_enough_weights:
          warnings.warn(
              f"Layer {layer.name} does not have enough weights to implement"
              f"{'per-channel ' if cluster_per_channel else ''}clustering."
              f" \nNo clustering was implemented for this layer.\n"
          )
      return has_enough_weights
  
file_path: model-optimization/tensorflow_model_optimization/python/core/clustering/keras/cluster.py
imports: import warnings
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_wrapper
  from tensorflow_model_optimization.python.core.clustering.keras import clustering_centroids
label_desc: Use image node_id 5 for calling a global function with example usage: layer_has_enough_weights_to_cluster(layer, number_of_clusters, cluster_per_channel) and returns: has_enough_weights